Application of supervised chemometric techniques and synchronized excitation-emission spectrofluorometric analysis for the verification of Maltese extra virgin olive oils

Abstract

The authentication of virgin olive oil samples usually requires the use of sophisticated and very expensive analytical techniques. In this study, the potential of fluorescence spectroscopy for the authentication and discrimination of Maltese extra virgin olive oils was carried out using synchronized excitation-emission spectroscopy. Samples were collected from various producers around the Maltese islands. Synchronous excitation emission spectra were collected in the region of 240–750 nm with wavelength intervals of 10, 30, 60, 80 120 and 185 nm and subjected to several supervised chemometric procedures. Partial least square regression, linear discriminate analysis, and artificial neural network were used to define the origin of the Maltese olive oil against olive oils derived from other neighboring countries in the Mediterranean region. After subjecting the spectroscopic data to different pretreatments and variable selection procedures results obtained evidenced a higher classification accuracy. This accuracy and predictability were highly dependent on the wave interval used and on the chemometric method used, however it was found that in general spectra obtained using d 10 nm were deemed the most appropriate, with PLS, ANN and LDA reaching 100% accuracy and predictability in discriminating Maltese extra virgin olive oils when using derivatized spectral transformations.